Concentric Ring Loss for Face Forgery Detection

The issue of detecting face forgeries has garnered significant interest in the field of computer vision, primarily driven by the growing social concerns of indistinguishable deepfake images. One of the primary obstacles encountered in the field of deepfake detection is enhancing the discriminative power of learned features. In this paper, we propose a Concentric Ring Loss (CRL) that aims to promote the learning of compressed intra-class features and separated inter-class features inside a model. Specifically, we apply margin penalties in both Euclidean and angular space separately, which serve to increase the separation between real and fake images. Moreover, we introduce a frequency-aware triplet network with a self-developed sample generation strategy, which provides efficient hard triplets for model training. Extensive experiments demonstrate the superiority of our methods over multiple datasets. We show that CRL consistently outperforms the state-of-the-art by a large margin.